Applying feature selection to the GA-FNN resulted in a more accurate prediction of biological activity.
It was found that there is no significant difference between selected attributes for KC1 after applying feature selection methods.
As shown in the previous studies, classification accuracy is improved by applying feature selection, and NB based classifier performs the best in the majority of the cases.
It is also proven that applying feature such as ISL to the contrast is more reasonable than the absolute value of a single pixel.
After applying feature reduction to separate feature-sets, different homogenous multi-class classifiers are applied to each feature-set.
During training stage, we first used simple z-score normalization to standardize the features [ 19] before applying feature reduction.
Table 1 shows that the prediction results of the 10-folds cross-validation test improved after applying feature selection.
From these results we can see that applying feature selection improved the predictive performance, albeit on a small scale.
Almost in all cases, the accuracy performance classifiers were improved after applying feature selections methods to the datasets.
In particular, we present recent efforts in improving and enhancing FEATURE's functional coverage and efficiency, and in applying FEATURE in novel ways.
